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Your search keyword '"Soldatova LN"' showing total 4 results
Start Over Author "Soldatova LN" Topic computational biology
4 results on '"Soldatova LN"'

1. Closed-loop cycles of experiment design, execution, and learning accelerate systems biology model development in yeast.

Author

Coutant A, Roper K, Trejo-Banos D, Bouthinon D, Carpenter M, Grzebyta J, Santini G, Soldano H, Elati M, Ramon J, Rouveirol C, Soldatova LN, and King RD

Subjects
Computational Biology, Gene Expression Regulation, Fungal, Robotics, Saccharomyces cerevisiae genetics, Saccharomyces cerevisiae metabolism, Software, Systems Biology
Abstract

One of the most challenging tasks in modern science is the development of systems biology models: Existing models are often very complex but generally have low predictive performance. The construction of high-fidelity models will require hundreds/thousands of cycles of model improvement, yet few current systems biology research studies complete even a single cycle. We combined multiple software tools with integrated laboratory robotics to execute three cycles of model improvement of the prototypical eukaryotic cellular transformation, the yeast ( Saccharomyces cerevisiae ) diauxic shift. In the first cycle, a model outperforming the best previous diauxic shift model was developed using bioinformatic and systems biology tools. In the second cycle, the model was further improved using automatically planned experiments. In the third cycle, hypothesis-led experiments improved the model to a greater extent than achieved using high-throughput experiments. All of the experiments were formalized and communicated to a cloud laboratory automation system (Eve) for automatic execution, and the results stored on the semantic web for reuse. The final model adds a substantial amount of knowledge about the yeast diauxic shift: 92 genes (+45%), and 1,048 interactions (+147%). This knowledge is also relevant to understanding cancer, the immune system, and aging. We conclude that systems biology software tools can be combined and integrated with laboratory robots in closed-loop cycles., Competing Interests: The authors declare no conflict of interest., (Copyright © 2019 the Author(s). Published by PNAS.)

Published
2019
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2. The automation of science.

Author

King RD, Rowland J, Oliver SG, Young M, Aubrey W, Byrne E, Liakata M, Markham M, Pir P, Soldatova LN, Sparkes A, Whelan KE, and Clare A

Subjects
Computers, Genomics, Programming Languages, Robotics, Saccharomyces cerevisiae enzymology, Saccharomyces cerevisiae growth & development, Saccharomyces cerevisiae metabolism, Software, Artificial Intelligence, Automation, Computational Biology, Enzymes genetics, Genes, Fungal, Saccharomyces cerevisiae genetics
Abstract

The basis of science is the hypothetico-deductive method and the recording of experiments in sufficient detail to enable reproducibility. We report the development of Robot Scientist "Adam," which advances the automation of both. Adam has autonomously generated functional genomics hypotheses about the yeast Saccharomyces cerevisiae and experimentally tested these hypotheses by using laboratory automation. We have confirmed Adam's conclusions through manual experiments. To describe Adam's research, we have developed an ontology and logical language. The resulting formalization involves over 10,000 different research units in a nested treelike structure, 10 levels deep, that relates the 6.6 million biomass measurements to their logical description. This formalization describes how a machine contributed to scientific knowledge.

Published
2009
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3. Are the current ontologies in biology good ontologies?

Author

Soldatova LN and King RD

Subjects
Biology trends, Biotechnology methods, Classification, Computational Biology trends, Computer Simulation, Databases, Genetic, Genetic Engineering, Humans, Information Storage and Retrieval, Models, Genetic, Models, Theoretical, Open Reading Frames, RNA metabolism, Biology methods, Computational Biology methods
Abstract

The failure of many bio-ontologies to follow international standards for ontology design and description is hampering their application and threatens to restrict their future use.

Published
2005
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4. The Ontology for Biomedical Investigations

Author

Bandrowski, A, Brinkman, R, Brochhausen, M, Brush, MH, Bug, B, Chibucos, MC, Clancy, K, Courtot, M, Derom, D, Dumontier, M, Fan, L, Fostel, J, Fragoso, G, Gibson, F, Gonzalez-Beltran, A, Haendel, MA, He, Y, Heiskanen, M, Hernandez-Boussard, T, Jensen, M, Lin, Y, Lister, AL, Lord, P, Malone, J, Manduchi, E, McGee, M, Morrison, N, Overton, JA, Parkinson, H, Peters, B, Rocca-Serra, P, Ruttenberg, A, Sansone, S-A, Scheuermann, RH, Schober, D, Smith, B, Soldatova, LN, Stoeckert, CJ, Taylor, CF, Torniai, C, Turner, JA, Vita, R, Whetzel, PL, Zheng, J, Xue, Y, and Xue, Y

Subjects
0301 basic medicine, Databases, Factual, Microarrays, Computer science, Interoperability, Social Sciences, lcsh:Medicine, Ontology (information science), Bioinformatics, 0302 clinical medicine, Sociology, Consortia, Medicine and Health Sciences, Psychology, ontologies, lcsh:Science, Data Management, Language, computer.programming_language, Protozoans, Multidisciplinary, Malarial Parasites, Web Ontology Language, Genomics, Semantics, Functional Genomics, 3. Good health, ontology for Biomedical Investigations (OBI), Bioassays and Physiological Analysis, Vertebrates, web ontology language, Ontology, Web resource, Research Article, biomedical investigations, Computer and Information Sciences, Research and Analysis Methods, Basic Formal Ontology, Birds, World Wide Web, Open Biomedical Ontologies, 03 medical and health sciences, Text mining, Ontologies, Parasitic Diseases, Genetics, Animals, Humans, Ontology for Biomedical Investigations, Internet, Metadata, Raptors, business.industry, lcsh:R, Organisms, Cognitive Psychology, Computational Biology, Biology and Life Sciences, Biological Ontologies, Parasitic Protozoans, Owls, 030104 developmental biology, Amniotes, Cognitive Science, lcsh:Q, business, computer, Software, 030217 neurology & neurosurgery, Neuroscience
Abstract

The Ontology for Biomedical Investigations (OBI) is an ontology that provides terms with precisely defined meanings to describe all aspects of how investigations in the biological and medical domains are conducted. OBI re-uses ontologies that provide a representation of biomedical knowledge from the Open Biological and Biomedical Ontologies (OBO) project and adds the ability to describe how this knowledge was derived. We here describe the state of OBI and several applications that are using it, such as adding semantic expressivity to existing databases, building data entry forms, and enabling interoperability between knowledge resources. OBI covers all phases of the investigation process, such as planning, execution and reporting. It represents information and material entities that participate in these processes, as well as roles and functions. Prior to OBI, it was not possible to use a single internally consistent resource that could be applied to multiple types of experiments for these applications. OBI has made this possible by creating terms for entities involved in biological and medical investigations and by importing parts of other biomedical ontologies such as GO, Chemical Entities of Biological Interest (ChEBI) and Phenotype Attribute and Trait Ontology (PATO) without altering their meaning. OBI is being used in a wide range of projects covering genomics, multi-omics, immunology, and catalogs of services. OBI has also spawned other ontologies (Information Artifact Ontology) and methods for importing parts of ontologies (Minimum information to reference an external ontology term (MIREOT)). The OBI project is an open cross-disciplinary collaborative effort, encompassing multiple research communities from around the globe. To date, OBI has created 2366 classes and 40 relations along with textual and formal definitions. The OBI Consortium maintains a web resource (http://obi-ontology.org) providing details on the people, policies, and issues being addressed in association with OBI. The current release of OBI is available at http://purl.obolibrary.org/obo/obi.owl. This work was supported by the National Institute of Allergy and Infectious Diseases (https:// www.niaid.nih.gov/Pages/default.aspx) R01AI081062 to YH, the National Institutes of Health (http://www. nih.gov/) R01GM093132, U01 DK 072473, P41 HG003619 and HHSN272201400030C to CJS, HHSN272201400028C to RHS, HHSN272201200010C and 1U19AI118626 to BP, the California Institute for Regenerative Medicine (https:// www.cirm.ca.gov) GC1R-06673-B to RHS, the National Science Foundation Division of Biological Infrastructure (http://www.nsf.gov) 1458400 to MCC

Published
2016

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